Method and apparatus for updating deep learning model

ABSTRACT

The disclosure discloses a method and apparatus for updating a deep learning model. An embodiment of the method comprises: executing following updating: acquiring a training dataset under a preset path, training a preset deep learning model based on the training dataset to obtain a new deep learning model; updating the preset deep learning model to the new deep learning model; increasing training iterations; determining whether a number of training iterations reaches a threshold of training iterations; stopping executing the updating if the number of training iterations reaches the threshold of training iterations; and continuing to execute the updating after an interval of a preset time length if the number of training iterations fails to reach the threshold of training iterations. This embodiment has improved the model updating efficiency.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Patent Application No.201710539193.1, filed with the State Intellectual Property Office of thePeople's Republic of China (SIPO) on Jul. 4, 2017, the content of whichis incorporated herein by reference in its entirety.

TECHNICAL FIELD

The disclosure relates to the field of computer technology, specificallyto the field of Internet technology, and more specifically to a methodand apparatus for updating a deep learning model.

BACKGROUND

A model, as an important concept in machine learning, simply speaking,refers to the mapping from a characteristic space to an output space,and generally is constituted by a hypothesis function and modelparameters. Commonly used models in industry include logistic regression(LR), gradient boosting decision tree (GBDT), support vector machine(SVM), deep neural network (DNN), and the like.

Deep learning is a branch of machine learning, and it's an algorithmthat attempts to perform higher-level abstraction on data using aplurality of processing layers containing a complicated structure orcomposed of multiple nonlinear transformations.

At present, the operations of training and updating the deep learningmodel are usually triggered manually by a user, thereby leading to lowefficiency in updating the deep learning model.

SUMMARY

An object of the disclosure is to provide a method and apparatus forupdating a deep learning model, to solve the technical problemsmentioned in the background part.

In a first aspect, an embodiment of the disclosure provides a method forupdating a deep learning model, the method including: executingfollowing updating: acquiring a training dataset under a preset path,training a preset deep learning model based on the training dataset toobtain a new deep learning model; updating the preset deep learningmodel to the new deep learning model; increasing a number of training;determining whether the number of training reaches a threshold of thenumber of training; stopping executing the updating if the number oftraining iterations reaches the threshold of training iterations; andcontinuing to execute the updating after an interval of a preset timelength if the number of training fails to reach the threshold of thenumber of training.

In some embodiments, the training dataset under the preset path isprovided with a dataset identifier containing a time stamp; and theacquiring a training dataset under a preset path includes: acquiring atraining dataset with the time stamp contained in the dataset identifiermatching a current time stamp under the preset path.

In some embodiments, the acquiring a training dataset with the timestamp contained in the dataset identifier matching a current time stampunder the preset path includes: calculating a similarity between thecurrent time stamp and the time stamp contained in a dataset identifierof each training dataset under the preset path, and determining a timestamp with a highest similarity to the current time stamp as a targettime stamp matching the current time stamp; and acquiring a trainingdataset with a dataset identifier containing the target time stamp underthe preset path.

In some embodiments, the preset deep learning model has a trainingscript corresponding to the preset deep learning model; and the traininga preset deep learning model based on the training dataset to obtain anew deep learning model includes: running the training script to trainthe preset deep learning model based on the training dataset to obtainthe new deep learning model.

In some embodiments, the training dataset under the preset path isgenerated through regularly running a preset training dataset generationcode.

In some embodiments, when the new deep learning model is obtained, theupdating further includes: saving the new deep learning model; and afterupdating the preset deep learning model to the new deep learning model,the updating further includes: sending a model updating prompt messageto a user the preset deep learning model attributed to, where the promptmessage includes at least one of following items of the new deeplearning model: a save path, a save title, and a dataset identifier ofthe employed training dataset.

In a second aspect, an embodiment of the disclosure provides anapparatus for updating a deep learning model, the apparatus including:an updating unit, configured for executing following updating: acquiringa training dataset under a preset path, training a preset deep learningmodel based on the training dataset to obtain a new deep learning model;updating the preset deep learning model to the new deep learning model;increasing a number of training; determining whether the number oftraining reaches a threshold of the number of training; stoppingexecuting the updating if the number of training reaches the threshold;and an execution unit, configured for continuing to execute the updatingafter an interval of a preset time length if the number of trainingfails to reach the threshold.

In some embodiments, the training dataset under the preset path isprovided with a dataset identifier containing a time stamp; and theupdating unit includes: an acquisition subunit, configured for acquiringa training dataset with the time stamp contained in the datasetidentifier matching a current time stamp under the preset path.

In some embodiments, the acquisition subunit includes: a determiningmodule, configured for calculating a similarity between the current timestamp and the time stamp contained in a dataset identifier of eachtraining dataset under the preset path, and determining a time stampwith a highest similarity to the current time stamp as a target timestamp matching the current time stamp; and an acquisition module,configured for acquiring a training dataset with a dataset identifiercontaining the target time stamp under the preset path.

In some embodiments, the preset deep learning model has a trainingscript corresponding to the preset deep learning model; and the updatingunit includes: a training subunit, configured for running the trainingscript to train the preset deep learning model based on the trainingdataset to obtain the new deep learning model.

In some embodiments, the training dataset under the preset path isgenerated through regularly running a preset training dataset generationcode.

In some embodiments, the updating unit further includes: a savingsubunit, configured for saving the new deep learning model; and asending subunit, configured for sending a model updating prompt messageto a user the preset deep learning model attributed to, where the promptmessage includes at least one of following items of the new deeplearning model: a save path, a save title, and a dataset identifier ofthe employed training dataset.

In a third aspect, an embodiment of the disclosure provides anelectronic device, the electronic device including: one or moreprocessors; and a memory for storing one or more programs, where the oneor more programs, when executed by the one or more processors, cause theone or more processors to implement the method according to any one ofthe implementations in the first aspect.

In a fourth aspect, an embodiment of the disclosure provides a computerreadable storage medium storing a computer program thereon, where theprogram implements, when executed by a processor, the method accordingto any one of the implementations in the first aspect.

The method and apparatus for updating a deep learning model provided bythe embodiments of the present disclosure, through: executing theupdating to acquire a training dataset under a preset path and train apreset deep learning model based on the training dataset to obtain a newdeep learning model; updating the preset deep learning model to the newdeep learning model to realize the upgrading of the preset deep learningmodel; progressively increasing the number of training while executingthe updating, to determine whether the number of training reaches athreshold of the number of training; stopping executing the updating ifthe number of training reaches the threshold; and continuing to executethe updating after a preset time interval if the number of trainingfails to reach the threshold, thereby may realize regular training andupdating of the preset deep learning model, improving the model updatingefficiency.

BRIEF DESCRIPTION OF THE DRAWINGS

By reading and referring to detailed description on the non-limitingembodiments in the following accompanying drawings, other features,objects and advantages of the disclosure will become more apparent:

FIG. 1 is a structural diagram of an exemplary system in which thedisclosure may be applied;

FIG. 2 is a flow chart of a method for updating a deep learning modelaccording to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of an application scenario of a method forupdating a deep learning model according to the disclosure;

FIG. 4 is a schematic structural diagram of an apparatus for updating adeep learning model according to an embodiment of the presentdisclosure; and

FIG. 5 is a schematic structural diagram of a computer system of anelectronic device suitable for implementing the embodiments of thepresent disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

The disclosure is further described in detail below in conjunction withthe accompanying drawings and embodiments. It may be appreciated thatthe embodiments described herein are only used for explaining thedisclosure, rather than limiting the disclosure. Furthermore, it shouldalso be noted that only the parts related to the disclosure are shown inthe accompanying drawings, for the ease of description.

It should also be noted that the embodiments in the present disclosureand the features in the embodiments may be combined with each other on anon-conflict basis. The present disclosure will be described below indetail with reference to the accompanying drawings and in combinationwith the embodiments.

FIG. 1 shows an exemplary system architecture 100 in which theembodiments of the method or apparatus for updating a deep learningmodel according to the present disclosure may be applied.

As shown in FIG. 1, the system architecture 100 may include a modelmanagement server 101, a network 102, and a data storage server 103. Thenetwork 102 is used for providing a communication link medium betweenthe model management server 101 and the data storage server 103. Thenetwork 102 may include a variety of connection types, such as a wiredcommunication link, a wireless communication link, or a fiber cable.

The data storage server 103 may be a server that provides a variety ofservices, e.g., a server for storing data such as a training dataset.

The model management server 101 may be a server that provides a varietyof services, e.g., a server for regularly executing training, updating,and other operations of the deep learning model.

It should be noted that the method for updating a deep learning modelprovided in the embodiments of the present disclosure is generallyexecuted by the model management server 101. Accordingly, the apparatusfor updating a deep learning model is generally set in the modelmanagement server 101.

It should be noted that, when the training dataset acquired by the modelmanagement server 101 is a pre-stored local training dataset in themodel management server 101, the system architecture 100 may not includethe data storage server 103.

It should be appreciated that the numbers of model management servers,networks and data storage servers in FIG. 1 are only illustrative. Theremay be any number of model management servers, networks and data storageservers based on implementation needs.

By further referring to FIG. 2, which shows a flow 200 of a method forupdating a deep learning model according to an embodiment of the presentdisclosure. The method for updating a deep learning model includes:

Step 201: executing the updating.

According to the present embodiment, an electronic device (e.g., themodel management server 101 as shown in FIG. 1) in which the method forupdating a deep learning model runs may execute the updating of a presetdeep learning model, and the updating may include:

Step 2011: acquiring a training dataset under a preset path, training apreset deep learning model based on the training dataset to obtain a newdeep learning model;

Step 2012: updating the preset deep learning model to the new deeplearning model;

Step 2013: increasing the number of training;

Step 2014: determining whether the number of training reaches athreshold of the number of training; and

Step 2015: stopping executing the updating if the number of trainingreaches the threshold.

In the present embodiment, the moment when the updating is executed forthe first time may be preset by a user to whom the preset deep learningmodel is attributed, or automatically set by the electronic device,which is not limited by the present embodiment in any way.

In step 2011, the electronic device may acquire the latest createdtraining dataset under the preset path. Here, the preset path may be apath in the electronic device, or may be a path in a server in remotecommunication connection with the electronic device (e.g., the datastorage server 103 as shown in FIG. 1). It should be noted that theelectronic device may retrieve characteristic information and estimateresult from each training data of the acquired training dataset, and theelectronic device may train the preset deep learning model using a deeplearning method based on the characteristic information and the estimateresult.

In step 2012, after receiving the new deep learning model, theelectronic device may update the preset deep learning model to the newdeep learning model, to enable the new deep learning model to be, e.g.,used for executing online data prediction.

In step 2013, an initial value of the number of training may be zero.When each time increasing the number of training, the electronic devicemay, for example, increase the number of training by 1.

In step 2014, the threshold of the number of training may be manuallyset, or be automatically set by the electronic device, which is notlimited by the embodiment in any way. The electronic device may, afterdetermining the number of training reaches the threshold, execute step2015. The electronic device may, if determining the number of trainingfails to reach the threshold, execute step 202.

In step 2015, the electronic device may, if determining the number oftraining reaches the threshold, stop executing the updating, i.e., nolonger training and updating the preset deep learning model.

In some optional implementations of the present embodiment, the trainingdataset under the preset path may be provided with a dataset identifiercontaining a time stamp; and the electronic device may acquire atraining dataset with a time stamp contained in a dataset identifiermatching a current time stamp under the preset path, to train the presetdeep learning model based on this training dataset. As an example, theelectronic device may search a training dataset with a time stampcontained in a dataset identifier identical to a current time stampunder the preset path, and the electronic device may determine the timestamp contained in the dataset identifier of the training datasetmatching the current time stamp.

In some optional implementations of the present embodiment, theelectronic device may calculate a similarity between the current timestamp and the time stamp contained in a dataset identifier of eachtraining dataset under the preset path, and determine a time stamp witha highest similarity to the current time stamp as a target time stamp;and the electronic device may acquire a training dataset with a datasetidentifier containing the target time stamp under the preset path, totrain the preset deep learning model based on this training dataset.

In some optional implementations of the present embodiment, the presetdeep learning model has a training script corresponding to the presetdeep learning model, and the electronic device may, through running thetraining script, train the preset deep learning model based on theacquired training dataset to obtain a new deep learning model.

In some optional implementations of the present embodiment, the trainingdataset under the preset path may be generated through regularly runninga preset training dataset generation code, and the preset trainingdataset generation code may be written and uploaded to the electronicdevice by the user.

In some optional implementations of the present embodiment, when theelectronic device obtains the new deep learning model, the updating mayfurther include: saving the new deep learning model. After updating thepreset deep learning model to the new deep learning model, the updatingmay further include: sending a model updating prompt message to theuser, where the prompt message may include at least one of followingitems of the new deep learning model: a save path, a save title, adataset identifier of the employed training dataset, and the like.

Step 202: continuing to execute the updating after a preset timeinterval if the number of training fails to reach the threshold of thenumber of training.

In the present embodiment, if the number of training fails to reach thethreshold, the electronic device may continue to execute the updatingafter a preset time interval (e.g., one day, half a month, and onemonth). Here, the preset time interval may be in a unit, such as minute,hour, day, and month, which is not limited by the embodiment in any way.

By further referring to FIG. 3, FIG. 3 is a schematic diagram of anapplication scenario of a method for updating a deep learning modelaccording to the present embodiment. In the application scenario of FIG.3, currently the number N of training on a preset deep learning model Ais assumed to be 9, a threshold of the number of training is assumed tobe 10, and a preset path saving a training dataset is assumed to be P.Assuming the current moment is a moment for triggering the executing theupdating of the preset deep learning model A, the server may executefollowing updating: firstly, as shown by a reference numeral 301, theserver may acquire a latest created training dataset under the presetpath P, and the server may train the preset deep learning model A basedon this training dataset to obtain a new deep learning model B; then, asshown by a reference numeral 302, the server may update the preset deeplearning model A to the new deep learning model B; then, as shown by areference numeral 303, the server may increase the number N of trainingby 1; then, as shown by a reference numeral 304, the server may comparethe number N of training with the threshold 10 of the number of trainingto determine whether the number N of training reaches the threshold 10;and finally, as shown by a reference numeral 305, the server maydetermine that the number N of training reaches the threshold 10, andthe server may stop executing the updating.

The method provided by the above embodiments of the present disclosure,through: executing the updating to acquire a training dataset under apreset path and train a preset deep learning model based on the trainingdataset to obtain a new deep learning model; updating the preset deeplearning model to the new deep learning model to realize the upgradingof the preset deep learning model; progressively increasing the numberof training while executing the updating, to determine whether thenumber of training reaches the threshold of the number of training;stopping executing the updating if the number of training reaches thethreshold; and continuing to execute the updating after a preset timeinterval if the number of training fails to reach the threshold, therebymay realize regular training and updating of the preset deep learningmodel, improving the model updating efficiency.

By further referring to FIG. 4, as implementations of the methods shownin the above figures, an embodiment of the present disclosure providesan apparatus for updating a deep learning model. The embodiment of theapparatus corresponds to the embodiment of the method shown in FIG. 2,and the apparatus may be specifically applied to a variety of electronicdevices.

As shown in FIG. 4, the apparatus 400 for updating a deep learning modelas shown in the embodiment includes: an updating unit 401 and anexecution unit 402. Here, the updating unit 401 is configured forexecuting following updating: acquiring a training dataset under apreset path, training a preset deep learning model based on the trainingdataset to obtain a new deep learning model; updating the preset deeplearning model to the new deep learning model; increasing the number oftraining; determining whether the number of training reaches a thresholdof the number of training; stopping executing the updating if the numberof training iterations reaches the threshold; and the execution unit 402is configured for continuing to execute the updating after a preset timeinterval if the number of training fails to reach the threshold.

According to the present embodiment, in the apparatus 400 for updating adeep learning model, specific processing of the updating unit 401 andthe execution unit 402 and technical effects brought thereby may berespectively referred to the relevant description of the steps 201 and202 in the embodiments corresponding to FIG. 2, and are not repeated anymore here.

In some optional implementations of the present embodiment, the trainingdataset under the preset path is provided with a dataset identifiercontaining a time stamp; and the updating unit 401 may include: anacquisition subunit (not shown in the figure), configured for acquiringa training dataset with the time stamp contained in the datasetidentifier matching a current time stamp under the preset path.

In some optional implementations of the embodiment, the acquisitionsubunit may include: a determining module (not shown in the figure),configured for calculating a similarity between the current time stampand the time stamp contained in a dataset identifier of each trainingdataset under the preset path, and determining a time stamp with ahighest similarity to the current time stamp as a target time stampmatching the current time stamp; and an acquisition module (not shown inthe figure), configured for acquiring a training dataset with a datasetidentifier containing the target time stamp under the preset path.

In some optional implementations of the embodiment, the preset deeplearning model has a training script corresponding to the preset deeplearning model; and the updating unit 401 may include: a trainingsubunit (not shown in the figure), configured for running the trainingscript to train the preset deep learning model based on the trainingdataset to obtain the new deep learning model.

In some optional implementations of the embodiment, the training datasetunder the preset path is generated through regularly running a presettraining dataset generation code.

In some optional implementations of the embodiment, the updating unit401 may further include: a saving subunit (not shown in the figure),configured for saving the new deep learning model; and a sending subunit(not shown in the figure), configured for sending a model updatingprompt message to a user to whom the preset deep learning modelattributed, where the prompt message includes at least one of followingitems of the new deep learning model: a save path, a save title, and adataset identifier of the employed training dataset.

The apparatus provided by the above embodiments of the disclosure,through: executing the updating to acquire a training dataset under apreset path and train a preset deep learning model based on the trainingdataset to obtain a new deep learning model; updating the preset deeplearning model to the new deep learning model to realize the upgradingof the preset deep learning model; progressively increasing the numberof training while executing the updating, to determine whether thenumber of training reaches a threshold of training iterations; stopsexecuting the updating if the number of training iterations reaches thethreshold of; and continuing to execute the updating after a preset timeinterval if the number of training fails to reach the threshold, therebymay realize regular training and updating of the preset deep learningmodel, improving the model updating efficiency.

By referring to FIG. 5, a schematic structural diagram of a computersystem 500 suitable for implementing the electronic device according tothe embodiments of the present disclosure is shown. The electronicdevice shown in FIG. 5 is only an example, and should not limit thefunctions and scope of application of the embodiments of the presentdisclosure in any way.

As shown in FIG. 5, the computer system 500 includes a centralprocessing unit (CPU) 501, which may execute various appropriate actionsand processes in combination with n a program stored in a read onlymemory (ROM) 502 or a program loaded from a storage part 508 into arandom access memory (RAM) 503. Various programs and data required byoperations of the system 500 are also stored in the RAM 503. The CPU501, the ROM 502 and the RAM 503 are connected to each other through abus 504. An input/output (I/O) interface 505 is also connected to thebus 504.

The following components are connected to the I/O interface 505: aninput portion 506 including a keyboard, a mouse etc.; an output portion507 comprising a cathode ray tube (CRT), a liquid crystal display device(LCD), a speaker etc.; a storage portion 508 including a hard disk andthe like; and a communication portion 509 comprising a network interfacecard, such as a LAN card and a modem. The communication portion 509performs communication processes via a network, such as the Internet. Adriver 510 is also connected to the I/O interface 505 as required. Aremovable medium 511, such as a magnetic disk, an optical disk, amagneto-optical disk, and a semiconductor memory, may be installed onthe driver 510, to facilitate the retrieval of a computer program fromthe removable medium 511, and the installation thereof on the storageportion 508 as needed.

In particular, according to embodiments of the present disclosure, theprocess described above with reference to the flow chart may beimplemented in a computer software program. For example, an embodimentof the present disclosure includes a computer program product, whichcomprises a computer program that is tangibly embedded in amachine-readable medium. The computer program comprises program codesfor executing the method as illustrated in the flow chart. In such anembodiment, the computer program may be downloaded and installed from anetwork via the communication portion 509, and/or may be installed fromthe removable media 511. The computer program, when executed by thecentral processing unit (CPU) 501, implements the above-mentionedfunctionalities as defined by the methods of the present disclosure.

It should be noted that the computer readable medium in the presentdisclosure may be computer readable signal medium or computer readablestorage medium or any combination of the above two. An example of thecomputer readable storage medium may include, but not limited to:electric, magnetic, optical, electromagnetic, infrared, or semiconductorsystems, apparatus, elements, or a combination any of the above. A morespecific example of the computer readable storage medium may include butis not limited to: electrical connection with one or more wire, aportable computer disk, a hard disk, a random access memory (RAM), aread only memory (ROM), an erasable programmable read only memory (EPROMor flash memory), a fiber, a portable compact disk read only memory(CD-ROM), an optical memory, a magnet memory or any suitable combinationof the above. In the present disclosure, the computer readable storagemedium may be any physical medium containing or storing programs whichcan be used by a command execution system, apparatus or element orincorporated thereto. In the present disclosure, the computer readablesignal medium may include data signal in the base band or propagating asparts of a carrier, in which computer readable program codes arecarried. The propagating signal may take various forms, including butnot limited to: an electromagnetic signal, an optical signal or anysuitable combination of the above. The signal medium that can be read bycomputer may be any computer readable medium except for the computerreadable storage medium. The computer readable medium is capable oftransmitting, propagating or transferring programs for use by, or usedin combination with, a command execution system, apparatus or element.The program codes contained on the computer readable medium may betransmitted with any suitable medium including but not limited to:wireless, wired, optical cable, RF medium etc., or any suitablecombination of the above.

The flow charts and block diagrams in the accompanying drawingsillustrate architectures, functions and operations that may beimplemented according to the systems, methods and computer programproducts of the various embodiments of the present disclosure. In thisregard, each of the blocks in the flowcharts or block diagrams mayrepresent a module, a program segment, or a code portion, said module,program segment, or code portion comprising one or more executableinstructions for implementing specified logic functions. It should alsobe noted that, in some alternative implementations, the functionsdenoted by the blocks may occur in a sequence different from thesequences shown in the figures. For example, any two blocks presented insuccession may be executed, substantially in parallel, or they maysometimes be in a reverse sequence, depending on the function involved.It should also be noted that each block in the block diagrams and/orflow charts as well as a combination of blocks may be implemented usinga dedicated hardware-based system executing specified functions oroperations, or by a combination of a dedicated hardware and computerinstructions.

The flow charts and block diagrams in the accompanying drawingsillustrate architectures, functions and operations that may beimplemented according to the systems, methods and computer programproducts of the various embodiments of the present disclosure. In thisregard, each of the blocks in the flowcharts or block diagrams mayrepresent a module, a program segment, or a code portion, said module,program segment, or code portion comprising one or more executableinstructions for implementing specified logic functions. It should alsobe noted that, in some alternative implementations, the functionsdenoted by the blocks may occur in a sequence different from thesequences shown in the figures. For example, any two blocks presented insuccession may be executed, substantially in parallel, or they maysometimes be in a reverse sequence, depending on the function involved.It should also be noted that each block in the block diagrams and/orflow charts as well as a combination of blocks may be implemented usinga dedicated hardware-based system executing specified functions oroperations, or by a combination of a dedicated hardware and computerinstructions.

The units involved in the embodiments of the present disclosure may beimplemented by means of software or hardware. The described units mayalso be provided in a processor, and for example, may be described as: aprocessor comprising an updating unit and an execution unit, where thenames of the units do not in some cases constitute a limitation to suchunits themselves. For example, the updating unit may also be describedas a “unit for executing the updating.”

In another aspect, the present disclosure further provides acomputer-readable storage medium. The computer-readable storage mediummay be the computer storage medium included in the apparatus in theabove described embodiments, or a stand-alone computer-readable storagemedium not assembled into the apparatus. The computer-readable storagemedium stores one or more programs. The one or more programs, whenexecuted by a device, cause the device to: executing following updating:acquire a training dataset under a preset path, train a preset deeplearning model based on the training dataset to obtain a new deeplearning model; update the preset deep learning model to the new deeplearning model; increase a number of training; determine whether thenumber of training reaches a threshold of the number of training; stopexecuting the updating if the number of training reaches the threshold;and continue to execute the updating after a preset time interval if thenumber of training fails to reach the threshold.

The above description only provides an explanation of the preferredembodiments of the present disclosure and the technical principles used.It should be appreciated by those skilled in the art that the inventivescope of the present disclosure is not limited to the technicalsolutions formed by the particular combinations of the above-describedtechnical features. The inventive scope should also cover othertechnical solutions formed by any combinations of the above-describedtechnical features or equivalent features thereof without departing fromthe concept of the disclosure. Technical schemes formed by theabove-described features being interchanged with, but not limited to,technical features with similar functions disclosed in the presentdisclosure are examples.

What is claimed is:
 1. A method for updating a deep learning model,comprising: executing following updating: acquiring a training datasetunder a preset path, training a preset deep learning model based on thetraining dataset to obtain a new deep learning model; updating thepreset deep learning model to the new deep learning model; increasing anumber of training; determining whether the number of training reaches athreshold of the number of training; stopping executing the updating ifthe number of training reaches the threshold; and continuing to executethe updating after a preset time interval if the number of trainingfails to reach the threshold.
 2. The method according to claim 1,wherein the training dataset under the preset path is provided with adataset identifier containing a time stamp; and the acquiring a trainingdataset under a preset path comprises: acquiring a training dataset withthe time stamp contained in the dataset identifier matching a currenttime stamp under the preset path.
 3. The method according to claim 2,wherein the acquiring a training dataset with the time stamp containedin the dataset identifier matching a current time stamp under the presetpath comprises: calculating a similarity between the current time stampand the time stamp contained in a dataset identifier of each trainingdataset under the preset path, and determining a time stamp with ahighest similarity to the current time stamp as a target time stampmatching the current time stamp; and acquiring a training dataset with adataset identifier containing the target time stamp under the presetpath.
 4. The method according to claim 1, wherein the preset deeplearning model has a training script corresponding to the preset deeplearning model; and the training a preset deep learning model based onthe training dataset to obtain a new deep learning model comprises:running the training script to train the preset deep learning modelbased on the training dataset to obtain the new deep learning model. 5.The method according to claim 1, wherein the training dataset under thepreset path is generated through regularly running a preset trainingdataset generation code.
 6. The method according to claim 1, whereinwhen the new deep learning model is obtained, the updating furthercomprises: saving the new deep learning model; and after updating thepreset deep learning model to the new deep learning model, the updatingfurther comprises: sending a model updating prompt message to a user thepreset deep learning model attributed to, wherein the prompt messagecomprises at least one of following items of the new deep learningmodel: a save path, a save title, and a dataset identifier of theemployed training dataset.
 7. An apparatus for updating a deep learningmodel, comprising: at least one processor; and a memory storinginstructions, the instructions when executed by the at least oneprocessor, cause the at least one processor to perform operations, theoperations comprising: executing following updating: acquiring atraining dataset under a preset path, training a preset deep learningmodel based on the training dataset to obtain a new deep learning model;updating the preset deep learning model to the new deep learning model;increasing a number of training; determining whether the number oftraining reaches a threshold of the number of training; stoppingexecuting the updating if the number of training reaches the threshold;and continuing to execute the updating after a preset time interval ifthe number of training fails to reach the threshold.
 8. The apparatusaccording to claim 7, wherein the training dataset under the preset pathis provided with a dataset identifier containing a time stamp; and theacquiring a training dataset under a preset path comprises: acquiring atraining dataset with the time stamp contained in the dataset identifiermatching a current time stamp under the preset path.
 9. The apparatusaccording to claim 8, wherein the acquiring a training dataset with thetime stamp contained in the dataset identifier matching a current timestamp under the preset path comprises: calculating a similarity betweenthe current time stamp and the time stamp contained in a datasetidentifier of each training dataset under the preset path, anddetermining a time stamp with a highest similarity to the current timestamp as a target time stamp matching the current time stamp; andacquiring a training dataset with a dataset identifier containing thetarget time stamp under the preset path.
 10. The apparatus according toclaim 7, wherein the preset deep learning model has a training scriptcorresponding to the preset deep learning model; and the training apreset deep learning model based on the training dataset to obtain a newdeep learning model comprises: running the training script to train thepreset deep learning model based on the training dataset to obtain thenew deep learning model.
 11. The apparatus according to claim 7, whereinthe training dataset under the preset path is generated throughregularly running a preset training dataset generation code.
 12. Theapparatus according to claim 7, wherein the updating further comprises:saving the new deep learning model; and after updating the preset deeplearning model to the new deep learning model, the updating furthercomprises: sending a model updating prompt message to a user the presetdeep learning model attributed to, wherein the prompt message comprisesat least one of following items of the new deep learning model: a savepath, a save title, and a dataset identifier of the employed trainingdataset.
 13. A non-transitory computer readable storage medium storing acomputer program, wherein the computer program, when executed by aprocessor, cause the processor to perform operations, the operationcomprising: executing following updating: acquiring a training datasetunder a preset path, training a preset deep learning model based on thetraining dataset to obtain a new deep learning model; updating thepreset deep learning model to the new deep learning model; increasing anumber of training; determining whether the number of training reaches athreshold of the number of training; stopping executing the updating ifthe number of training reaches the threshold; and continuing to executethe updating after a preset time interval if the number of trainingfails to reach the threshold.